Optimizing A Multi-National Supply Chain

Overview

Customers are demanding. They expect goods to arrive on time, at a minimum cost. Because retail margins are razor thin, keeping up with customer demands can be disastrous for profits if inefficiencies exist in your business. Maximizing delivery network efficiency and optimizing delivery schedules is a complex but tractable problem, elegantly handled by modern data science techniques.

The Challenge

This Consumer Retail equipment manufacturing company had an exceptionally complicated supply chain, with a distribution model spidered across an intertwined United States delivery chain. For the past fifty years their supply chain has evolved based on requests from sales managers to service customers from local facilities and requests from management to ship full trucks directly off the line. The result was a delivery network optimized to support each of these requests composed of facilities of varying size and operational capacity and trucks waiting to be filled on the line.

The data over fifty years of operation was messy and segregated, so the regional planners often relied on localized tribal knowledge to get customers product in time to meet SLAs. This intuition-driven delivery mechanism was ripe for system wide optimization to improve overall efficiency and reduce network costs.

Our Solution

Expero reduced freight and distribution costs over 12% by consolidating their supply chain from dozens of small, non-optimally placed distribution centers to an efficient six distribution facility network. In the new delivery network design, the company is able to ship goods to customers more cost effectively by guaranteeing a lower total freight cost. We then built a system to automatically adapt to changing customer demand in real time, to manage inventory across the new distribution network, pack freight trucks according to the current customer demand, and ship the goods with optimal delivery scheduling. All of this while meeting customer delivery SLAs.